Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

Gao, Hongliang; Li, Xiaoling; Gao, Chao; Wu, Jie

doi:10.1155/2021/5536573

Cited by 20 publications

(12 citation statements)

References 43 publications

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“…In order to further demonstrate the superiority of the method designed in this paper, the method designed in this paper is compared with an improved PID controller based on RBF neural network supervision control strategy (IPID-RBF) method designed in Gao et al 27 The specific results are shown in the tables below:…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The results show that this control method reduces the overshoot, accelerates the response speed, and improves the dynamic performance of the system. In the study of Gao et al 27 an improved method combining RBF neural network with PID control was proposed and applied to an inverted pendulum system. Compared with other methods, the results show that the overshoot of the tracking signal is further reduced and the response speed is further improved.…”

Section: Introductionmentioning

confidence: 99%

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Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Gao

Xiong

2022

Advances in Mechanical Engineering

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Considering the contradiction among the response speed, overshoot and stability of system when the motor load system adopts PID control, a control strategy combining RBF (Radial Basis Function) neural network supervisory control and expert PID control is designed to effectively improve this problem in this paper. First of all, the related algorithms of RBF neural network supervisory control composed of RBF neural network and PID control (RSC-PID) is introduced. This method can make the motor load system reach a steady state faster than simple PID control. But RSC-PID is also unsatisfactory in terms of overshoot. Based on the RSC-PID control method, a hybrid controller combining RBF neural network supervisory control and expert PID control (RSC-EPID) is proposed. This method combines RSC-PID control theory with expert PID control ideas, further improves the stability and rapidity of the system, reduces the overshoot. Moreover, when the input signal is a time-varying signal with interference, the motor load system shows better anti-interference performance after using RSC-EPID. The simulation results show that RSC-EPID control improves the tracking effect of the output signal of the motor load system, ensures the stability of the system, and improves the performance of the system.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Gao

Xiong

2022

Advances in Mechanical Engineering

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“…The neural network has been proved to be a powerful control tool for multivariable and nonlinear systems [19]. However, the structure and function of traditional neural network are difficult to relate to the dynamic indicators in the control system.…”

Section: Pid Neural Networkmentioning

confidence: 99%

Fractional-order Learning Algorithm for PID Neural Network Decoupling Control Based on Sparrow Search Algorithm

Ding

Yuan

et al. 2021

Preprint

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Research on control of multi-variable system with strong coupling has been a significant issue in industry. To accurately eliminate the coupling between system variables and improve the control effect, decoupling control techniques are investigated. In this paper, a decoupling control scheme based on fractional-order proportion integration differentiation neural network and sparrow search algorithm (SSA-FPIDNN) is proposed, where sparrow search algorithm is employed to derive the optimal initial weights, preventing the weights from falling into the local optimum, while the fractional-order algorithm is used to correct its connection weights to improve control accuracy. Compared with traditional PIDNN, the proposed SSA-FPIDNN has better decoupling control performance, and the tracking time can be reduced significantly. Numerical simulation and engineering examples verified its effectiveness.

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“…Therefore, in order to make the pendulum stay upward steadily, the velocity of the cart needs to be controlled. An inverted pendulum has characteristics as nonlinear [5], unstable [6], under-actuated system [7][8], multivariable system [9], and fast dynamic system [10]. The pendulum stick might fall easily even without the cart moving [11].…”

Section: Introductionmentioning

confidence: 99%

Backstepping Sliding Mode Control for Inverted Pendulum System with Disturbance and Parameter Uncertainty

Ma’arif

Márquez-Vera²,

Mahmoud³

et al. 2021

Journal of Robotics and Control

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The inverted pendulum system is highly popular in control system applications and has the characteristics of unstable, nonlinear, and fast dynamics. A nonlinear controller is needed to control a system with these characteristics. In addition, there are disturbances and parameter uncertainty issues to be solved in the inverted pendulum system. Therefore, this study uses a nonlinear controller, which is the backstepping sliding mode control. The controller is robust to parameter uncertainty and disturbances so that it is suitable for controlling an inverted pendulum system. Based on testing with step and sine reference signals without interference, the controller can stabilize the system well and has a fast response. In testing with disturbances and mass uncertainty, the backstepping sliding mode controller is robust against these changes and able to make the system reach the reference value. Compared with sliding mode control, backstepping sliding mode control has a better and more robust response to disturbances and parameter uncertainty.

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Neural Network Supervision Control Strategy for Inverted Pendulum Tracking Control

Cited by 20 publications

References 43 publications

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Research on a hybrid controller combining RBF neural network supervisory control and expert PID in motor load system control

Fractional-order Learning Algorithm for PID Neural Network Decoupling Control Based on Sparrow Search Algorithm

Backstepping Sliding Mode Control for Inverted Pendulum System with Disturbance and Parameter Uncertainty

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